Data retention policy explained
A data retention policy is a company's rulebook for how long it keeps data and when that data must be deleted, anonymized, or archived.
What Is a Data Retention Policy?
By early 2025, cumulative GDPR fines had reached roughly €5.65 billion, with 2024 alone adding €310 million against LinkedIn and €251 million against Meta, much of it tied to how those companies collected, used, and held onto personal data. Many of those enforcement actions involved organizations keeping or managing personal data longer than they could justify.
This guide explains what a data retention policy is, what it should include, common retention periods, and how to build one.
Key takeaways
- A data retention policy defines what data you keep, how long you keep it, and how you dispose of it.
- Retention rules stem from laws like GDPR, CCPA, HIPAA, and SOX, each with different timelines.
- Keeping data longer than necessary raises breach exposure, storage costs, and legal risk.
- Strong policies classify data by type, assign retention periods, and automate deletion.
- Retention periods vary widely: seven years for tax records, but only as long as needed for general personal data.
- Documenting retention practices in your privacy policy is often a legal requirement, not just good practice.
What is a data retention policy?
A data retention policy defines what data an organization keeps, how long each category is retained, and whether it is deleted, anonymized, or archived when that period ends.
For a shorter version of this definition, see Clym's data retention policy glossary entry.
Most privacy laws do not hand you a fixed number of years and call it done. Instead, they expect you to justify your own retention periods based on why you collected the data in the first place, then document and follow through on that reasoning.
Why does a data retention policy matter?
A documented retention policy matters for a few concrete reasons:
- It limits breach exposure. Data you no longer keep cannot be compromised.
- It supports GDPR, CCPA, and similar laws. Many privacy laws require organizations to keep personal data only as long as necessary.
- It reduces storage costs. Regular deletion prevents unnecessary data from accumulating.
- It speeds up audits and legal discovery. Less outdated data means faster reviews and investigations.
What should a data retention policy include?
A data retention policy should be specific enough that someone outside the legal team could read it and know exactly what to do. At minimum, it should cover:
- Data classification and scope: The types of data the policy covers, such as customer, employee, financial, and log data
- Retention periods: How long each type of data is kept and why
- Storage and access rules: Where data is stored and who can access it
- Deletion, anonymization, and archiving: What happens when a retention period ends
- Legal hold exceptions: When routine deletion is paused for litigation, audits, or investigations
- Ownership and review: Who is responsible for the policy and how often it is reviewed
How long should you keep data? Standard retention periods by data type
There is no single retention period that applies to every type of data. Instead, most organizations work from a table like the one below, then adjust based on the regulations that actually apply to them.
Data type | Typical retention period | Governing framework |
|---|---|---|
Tax and financial records | 7 years | IRS guidance, SOX |
Employee records | Duration of employment, plus several years | State labor law, OSHA |
Protected health information (PHI) | At least 6 years | HIPAA |
Payment card data | Only as long as necessary to process the transaction | PCI DSS |
General personal data (EU/UK) | No fixed period; only as long as necessary for the stated purpose | GDPR |
General personal data (California) | Only as long as reasonably necessary and proportionate | CCPA / CPRA |
These figures are common starting points, not guarantees. Retention obligations vary by jurisdiction, industry, and contract, so confirm specifics with legal counsel before finalizing your own schedule.
Data retention policy vs. related terms
People often use “data retention,” “data retention policy,” and “retention period” interchangeably, but they describe different things.
Term | What it means |
|---|---|
Data retention | The general practice of keeping data for a defined period before deleting, anonymizing, or archiving it |
Data retention policy | The documented rules that govern that practice: what is kept, for how long, and what happens next |
Retention period | The specific length of time set for a given category of data |
Data minimization | A related but separate principle: limiting what you collect in the first place, not just how long you keep it |
Data archiving | Moving inactive data to lower-cost, long-term storage instead of deleting it outright |
How to build a data retention policy: a step-by-step framework
Building a data retention policy is not a one-person job, and it is not something you write once and forget. Here is the process most organizations follow:
- Inventory your data. Identify what data you collect and where it is stored
- Classify your data. Group it into categories such as customer, employee, financial, and marketing data
- Identify legal requirements. Determine which laws or regulations apply to each data category
- Set retention periods. Define how long each category should be kept based on legal and business requirements
- Define disposal procedures. Specify whether data is deleted, anonymized, or archived when retention periods expire
- Include legal holds. Pause routine deletion when required for litigation, audits, or investigations
- Assign ownership. Identify who is responsible for maintaining the policy and reviewing it regularly
- Document and communicate the policy. Publish it, reference it in your privacy policy where appropriate, and ensure relevant teams understand their responsibilities
Keeping a retention policy accurate as your business grows is the hard part. Clym's policy management solution helps you document retention rules and publish them consistently across your privacy and cookie policy as your practices change.
Real-world data retention policy examples
Take a mid-size e-commerce retailer. It keeps active customer account data as long as the account remains open, deletes personal data within 30 days of account closure to comply with GDPR and CCPA, and retains transaction records for 7 years to satisfy tax and accounting requirements.
A healthcare provider faces stricter minimums. Patient records stay on file for at least 6 years under HIPAA, sometimes longer depending on state law, while marketing consent records are reviewed and refreshed on a shorter cycle tied to opt-in expiration.
Data retention policy best practices
Tie every retention period to a documented reason. “We might need it someday” is not a defensible policy.
Automate enforcement wherever possible. Manual deletion does not scale and tends to fall apart under pressure.
Build legal holds into the workflow, not around it. Retrofitting a hold process during active litigation is far riskier than having one ready.
Review the policy at least once a year. Laws change, and so does the data you collect.
Include backups and shadow systems. Retention rules that stop at your production database miss a large share of your actual risk.
Get legal, IT, and business teams in the room together. A policy written by one department in isolation rarely survives contact with reality.
Common data retention mistakes
Even well-intentioned teams run into the same handful of problems:
Keeping everything indefinitely, “just in case.” This is the single biggest driver of unnecessary breach exposure.
Using one retention period for all data types. A blanket policy is rarely defensible once you look closely at what it actually covers.
Skipping the documentation. Without a written rationale, you cannot explain later why data was kept or deleted at a given time.
Forgetting backups and exports. Spreadsheets, backups, and old SaaS tools often sit outside the official retention process entirely.
Letting the policy go stale. A retention policy from three years ago rarely reflects what your business collects today.
How Clym supports your data retention policy
Clym helps organizations document retention practices, publish consistent privacy disclosures, and manage deletion-related data subject requests.
Retention information, along with the rest of your policy content, can be published through the Control Center and displayed consistently through your Governance Portal, rather than manually copied across your website every time practices change.
Conclusion
A data retention policy is not just a compliance formality. A strong data retention policy helps organizations reduce legal risk, lower storage costs, and manage personal data more consistently.
The framework does not need to be complicated: inventory your data, classify it, assign defensible retention periods, and review the policy regularly as your business and the laws around it change. The good news is you do not have to build or maintain it entirely by hand.
Commonly asked questions
A data retention policy is a documented set of rules that defines what data an organization collects, how long it keeps each type, and what happens to it once that period ends, whether that is deletion, anonymization, or archiving. It supports compliance, reduces storage costs, and lowers breach exposure.
A good data retention policy ties every retention period to a specific legal, regulatory, or business reason, covers all the data types an organization actually holds (including backups), assigns clear ownership, and gets reviewed at least once a year so it keeps pace with changing laws and business practices.
It depends on the data type and the laws that apply. Tax and financial records are commonly kept around 7 years, healthcare records at least 6 years under HIPAA, and general personal data only as long as it serves the purpose it was collected for under GDPR and CCPA.
The purpose is to give an organization a defensible, consistent way to manage data throughout its lifecycle. It sets out what gets kept, for how long, and why, so teams avoid both the risk of over-retaining sensitive data and the risk of deleting records they are still legally required to keep.
Data classification tools, automated deletion workflows, and records management systems all help enforce retention rules at scale. On the documentation side, platforms like Clym help you draft, publish, and keep retention disclosures consistent across your privacy and cookie policy as your practices evolve.
When a retention period ends, the data should be deleted, anonymized, or moved to archival storage, unless a legal hold or active dispute requires it to be kept longer. Backups containing that data should be addressed too, since retention rules apply there as well.
Adam is the Head of Digital Marketing at Clym, where he leverages his diverse expertise in marketing to support businesses with their compliance needs and drive awareness about data privacy and web accessibility. As one of the company’s original team members, Adam has been instrumental in shaping its journey from the very beginning. When he’s not diving into marketing strategies, Adam can be found cheering on his favorite sports teams or enjoying fishing.
